The IMDb Movie Reviews dataset is a binary sentiment analysis dataset consisting of 50,000 reviews from the Internet Movie Database (IMDb) labeled as positive or negative. The dataset contains an even number of positive and negative reviews. Only highly
IMDb is the world's most popular and authoritative source for movie, TV and celebrity content. Find ratings and reviews for the newest movie and TV shows. Get personalized recommendations, and learn where to watch across hundreds of streaming providers.
sampleSubmission - A comma-delimited sample submission file in the correct format.以逗号分隔的⽰例提交⽂件，要求提交的格式必须正确。Data fields id - Unique ID of each review 每个评论的唯⼀id。sentiment - Sentiment of the review; 1 for positive reviews and 0 for negative reviews 评论的情绪...
The labeled data set consists of 50,000 IMDB movie reviews, specially selected for sentiment analysis. The sentiment of reviews is binary, meaning the IMDB rating < 5 results in a sentiment score of 0, and rating >=7 have a sentiment score of 1. No individual movie has more than 30 rev...
sentiment- Sentiment of the review; 1 for positive reviews and 0 for negative reviews 评论的情绪,正面评价为1、负面评价为0 review- Text of the review 评论的文本内容。 IMDB影评得分估计竞赛任务一共为参赛者提供了4份不同的数据文件,其中包括: ...
简介:Dataset之IMDB影评数据集:IMDB影评数据集的简介、下载、使用方法之详细攻略 IMDB影评数据集的简介 标签数据集包含5万条IMDB影评,专门用于情绪分析。评论的情绪是二元的,这意味着IMDB评级< 5导致情绪得分为0,而评级>=7的情绪得分为1。没有哪部电影的评论超过30条。标有training set的2.5万篇影评不包括与2.5万...
【摘要】 Dataset之IMDB影评数据集:IMDB影评数据集的简介、下载、使用方法之详细攻略 目录 IMDB影评数据集的简介 File descriptions Data fields IMDB影评数据集的下载 IMDB影评数据集的使用方法 IMDB影评数据集的简介 标签数据集包含5万条IMDB影评,专门用于情绪分析。评... ...
IMDB Movie Reviews Sentiment Dataset This dataset containsCSV versionsof the Large Movie Review dataset by Maas, et al. (2011) from its original Stanford AI Repository. It contains 50k highly polar movie reviews, evenly split to 25k positives and 25k negatives. Each sample is labeled with a 0...
SQuAD(Stanford Question Answering Dataset)是问答任务的数据集,包括SQuAD v1.1和SQuAD v2.0两个版本。任务描述如下: SQuAD v1.1:给定一个问题和一段文本,预测答案在文本中的位置。 SQuAD v2.0:与SQuAD v1.1类似,但允许问题没有答案,使问题更具现实性。 对于SQuAD v1.1,输入格式为[CLS]+问题+[SEP]+段落信息...
(model=model,args=training_args,train_dataset=tokenized_datasets['train'],eval_dataset=tokenized_datasets['test'],callbacks=[swanlab_callback],)# 训练模型trainer.train()# 保存模型model.save_pretrained('./sentiment_model')tokenizer.save_pretrained('./sentiment_model')# 测试模型test_reviews=["I ...